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GCP To AWS

ClineOfficialPublished by Cline

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality
— the description by Cline

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The gcp-to-aws skill provides a deterministic, multi-phase process to migrate Google Cloud Platform (GCP) workloads to AWS. It supports infrastructure migration via Terraform analysis and AI workload migration by mapping models to Amazon Bedrock. The skill enforces a strict state machine, requiring discovery, clarification, design, estimation, and generation phases to ensure a reliable migration path. It includes specific logic for handling hybrid stacks, AI provider transitions, and complex infrastructure clustering.

What it does

  • Discovers GCP resources from Terraform, application code, or billing data.
  • Clarifies migration requirements through a structured questionnaire.
  • Designs AWS architecture based on GCP workload types and AI model requirements.
  • Estimates costs for the proposed AWS infrastructure and AI services.
  • Generates migration artifacts including Terraform configurations and AI provider adapters.
  • Provides specialized guidance for migrating AI workloads from Gemini or OpenAI to Amazon Bedrock.

When to use it

  • When you need to migrate existing GCP infrastructure to AWS.
  • When you are looking to move AI-driven applications from GCP or OpenAI to Amazon Bedrock.
  • When you have Terraform files or billing exports and need a structured migration plan.
  • When you require a step-by-step, validated approach to re-platforming cloud services.

Advantages

  • Enforces a rigorous, fail-closed state machine to prevent skipped steps.
  • Provides intelligent mapping of GCP services to AWS equivalents.
  • Includes specific logic for AI workload migration and model compatibility.
  • Supports multi-signal discovery using IaC, code, and billing data.
  • Ensures consistent artifact generation through a structured 6-phase process.

Usage examples

“Migrate my GKE and Cloud SQL setup to AWS.”
The agent initiates the discovery phase, analyzing your Terraform files to identify GKE and Cloud SQL resources, then guides you through the clarification and design phases for EKS and RDS.
“I need to move my LangChain app from OpenAI to Bedrock.”
The agent identifies the AI workload, selects the best-fit Bedrock model family, and generates the necessary provider adapters and migration documentation.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/cline/gcp-to-aws/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gcp-to-aws.zip -d .agents/skills

Documentation by Cline

GCP-to-AWS Migration Skill

Philosophy

  • Re-platform by default: Select AWS services that match GCP workload types (e.g., Cloud Run → Fargate, Cloud SQL → RDS).

  • Dev sizing unless specified: Default to development-tier capacity (e.g., db.t4g.micro, single AZ). Upgrade only on user direction.

  • No human one-time migration costs: Do not present human labor, professional services, or people-time work as dollar estimates or "one-time migration cost" budget categories. Vendor charges grounded in data (for example GCP data transfer egress in the infra estimate when billing exists) are allowed.

  • Multi-signal approach: Design phase adapts based on available inputs — Terraform IaC for infrastructure, billing data for service mapping, and app code for AI workload detection.

  • BigQuery / google_bigquery_*: The skill does not recommend a specific AWS analytics or warehouse service. During Clarify, if discovery shows BigQuery (IaC google_bigquery_* and/or billing rows for BigQuery), you must surface the specialist advisory before Design (see references/phases/clarify/clarify.md). Design output uses Deferred — specialist engagement; keep directing the user to their AWS account team and/or a data analytics migration partner through Design, Estimate, and docs (see references/phases/design/design-infra.md BigQuery specialist gate).


Definitions

  • "Load" = Read the file using the Read tool and follow its instructions. Do not summarize or skip sections.

  • $MIGRATION_DIR = The run-specific directory under .migration/ (e.g., .migration/0226-1430/). Set during Phase 1 (Discover).


Context Loading Rules

Each phase loads reference files on demand. To keep per-turn context manageable and prevent instruction-following degradation:

  • Budget: Each phase should load no more than ~800 lines of instructions (excluding user artifacts like JSON profiles and MCP tool results).

  • Conditional loading: Reference files with trigger conditions (e.g., agentic_profile.is_agentic == true) MUST NOT be loaded unless the condition is met. Do not speculatively load files.

  • No duplication: Model mapping tables, pricing data, and shared warnings exist in one canonical file. Other files reference them; they do not copy them inline.

  • Progressive depth: Phase orchestrators (design.md, generate.md) contain short routing logic that points to detailed sub-files. Load the sub-file only when its path is selected.

Conditional reference files (load ONLY when condition is true):

FileCondition
design-refs/ai-gemini-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "gemini" or "both"
design-refs/ai-openai-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "openai" or "both"
design-refs/ai-anthropic-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "anthropic"
design-refs/ai.mdai-workload-profile.json exists AND summary.ai_source = "other"
design-refs/design-ref-harness.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "harness"
design-refs/design-ref-agentic-to-agentcore.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "strands"
shared/retarget-gotchas.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "retarget"

When adding new reference files, verify the phase's total loaded instructions remain under budget. If a new file would exceed ~800 lines when combined with other loaded refs, split it or make it conditional.

Hybrid stack budget warning:

When both gcp-resource-inventory.json AND ai-workload-profile.json exist, the combined design refs will approach the ~800-line budget. Output this warning to the user before loading the AI design refs:

"⚠️ This is a large hybrid stack (infrastructure + AI workloads). To ensure complete and accurate recommendations, consider running the migration in two separate passes:

Pass 1 — Infrastructure: Run with only your Terraform files to get infra mapping, Terraform generation, and cost estimates.

Pass 2 — AI workloads: Run with only your application code to get Bedrock model recommendations, provider adapters, and AI migration artifacts.

Continue with the combined run? (Y/N)"

If the user chooses to continue, proceed with the combined run. Load AI refs after infra refs to preserve infra instruction fidelity. If the user declines, stop and instruct them to re-run with a single input source type.

This warning is advisory only — it does not block the run.


Prerequisites

User must provide at least one GCP source:

  • Terraform IaC: .tf files (with optional .tfvars, .tfstate)

  • Application code: Source files with GCP SDK or AI framework imports

  • Billing data: GCP billing/cost/usage export files (CSV or JSON)

If none of the above are found, stop and ask user to provide at least one source type.


State Machine

This is the execution controller. After completing each phase, consult this table to determine the next action.

Current StateConditionNext Action
discoverphases.discover != "completed"Load references/phases/discover/discover.md
clarifyphases.discover == "completed" AND phases.clarify != "completed"Load references/phases/clarify/clarify.md
designphases.clarify == "completed" AND phases.design != "completed"Load references/phases/design/design.md
estimatephases.design == "completed" AND phases.estimate != "completed"Load references/phases/estimate/estimate.md
generatephases.estimate == "completed" AND phases.generate != "completed"Load references/phases/generate/generate.md
completephases.generate == "completed" AND phases.feedback == "pending"Set phases.feedback to "completed" (user had two chances), then migration complete
completephases.generate == "completed" AND phases.feedback == "completed"Migration planning complete

How to determine current state (deterministic):

  1. Read $MIGRATION_DIR/.phase-status.json

  2. If current_phase exists, use it (must match one of: discover, clarify, design, estimate, generate, complete)

  3. Otherwise use ordered phase evaluation: discover → clarify → design → estimate → generate

  4. Pick the first phase in that order where phases.<phase> != "completed"; if none, state is complete

Phase gate checks: If prior phase incomplete, do not advance (e.g., cannot enter estimate without completed design).

Clarify is mandatory: Do not load references/phases/design/design.md, references/phases/estimate/estimate.md, or references/phases/generate/generate.md unless $MIGRATION_DIR/.phase-status.json exists and phases.clarify is exactly "completed". A preferences.json file alone is not sufficient proof that Clarify ran. If the user asks to skip Clarify or jump straight to Design, cost estimate, or artifact generation, refuse briefly, then load references/phases/clarify/clarify.md and run Phase 2. There is no exception for "quick" or "obvious" migrations.

Feedback checkpoints: Feedback is not a sequential phase — it is offered at two interleaved checkpoints (after Discover and after Estimate). See the Feedback Checkpoints section below for details.

Handoff Gate Orchestration (Fail Closed)

Load references/shared/handoff-gates.md when executing any phase completion step.

  1. Single $MIGRATION_DIR: Use one run directory for the entire migration. Do not mix artifacts across .migration/*/ sessions.

  2. Re-read from disk: Before each phase (and before each handoff gate), Read required artifacts from $MIGRATION_DIR/. Do not rely on chat memory.

  3. Advance only on HANDOFF_OK: A phase is complete only when its orchestrator emits HANDOFF_OK | phase=<name> | artifacts=.... Do not load the next phase without it.

  4. On GATE_FAIL: Output the failure line(s) to the user in plain language. Do NOT modify artifacts to pass the gate. Do NOT continue to the next phase. Tell the user which phase to re-run.

  5. Re-entry: Re-running an earlier phase after downstream phases completed requires explicit user confirmation; downstream phases must be reset to "pending". See handoff-gates.md re-entry table.

Generate phase additionally loads references/shared/validate-artifacts.md before writing migration-report.html.


State Validation

When reading $MIGRATION_DIR/.phase-status.json, validate before proceeding:

  1. Multiple sessions: If multiple directories exist under .migration/, list them with their phase status and ask: [A] Resume latest, [B] Start fresh, [C] Cancel.

  2. Invalid JSON: If .phase-status.json fails to parse, STOP. Output: "State file corrupted (invalid JSON). Delete the file and restart the current phase."

  3. Unrecognized phase: If phases object contains a phase not in {discover, clarify, design, estimate, generate, feedback}, STOP. Output: "Unrecognized phase: [value]. Valid phases: discover, clarify, design, estimate, generate, feedback."

  4. Unrecognized status: If any phases.* value is not in {pending, in_progress, completed}, STOP. Output: "Unrecognized status: [value]. Valid values: pending, in_progress, completed."

  5. Invalid current_phase (if present): If current_phase is not in {discover, clarify, design, estimate, generate, complete}, STOP. Output: "Unrecognized current_phase: [value]. Valid values: discover, clarify, design, estimate, generate, complete."

  6. Out-of-order completion: For ordered phases [discover, clarify, design, estimate, generate], if any later phase is "completed" while an earlier phase is not "completed", STOP. Output: "Inconsistent phase ordering detected. Reconcile .phase-status.json before resuming."

  7. Multiple active phases: Across core phases {discover, clarify, design, estimate, generate}, at most one phase may be "in_progress". If >1, STOP. Output: "Multiple phases are in_progress. Keep only one active phase before resuming."


State Management

Migration state lives in $MIGRATION_DIR (.migration/[MMDD-HHMM]/), created by Phase 1 and persisted across invocations.

.phase-status.json schema:

{
  "migration_id": "0226-1430",
  "last_updated": "2026-02-26T15:35:22Z",
  "current_phase": "design",
  "phases": {
    "discover": "completed",
    "clarify": "completed",
    "design": "in_progress",
    "estimate": "pending",
    "generate": "pending",
    "feedback": "pending"
  }
}

Status values: "pending" → "in_progress" → "completed". Never goes backward. For core phases (discover, clarify, design, estimate, generate), at most one phase may be "in_progress" at any time. current_phase is optional but recommended; when present it is authoritative.

The .migration/ directory is automatically protected by a .gitignore file created in Phase 1.

Phase Status Update Protocol

Use read-merge-write updates for .phase-status.json:

  1. Read the current file before every update.

  2. Change only the phase keys being advanced and last_updated.

  3. Keep prior completed phases unchanged.

  4. Set current_phase to the next deterministic phase (or complete after generate).

  5. Write the full file in the same turn as your final phase work message.

Example — after completing the Clarify phase, write $MIGRATION_DIR/.phase-status.json with:

{
  "migration_id": "MMDD-HHMM",
  "last_updated": "2026-02-26T15:35:22Z",
  "current_phase": "design",
  "phases": {
    "discover": "completed",
    "clarify": "completed",
    "design": "pending",
    "estimate": "pending",
    "generate": "pending",
    "feedback": "pending"
  }
}

Replace MMDD-HHMM with the actual migration ID, generate the last_updated ISO 8601 UTC timestamp yourself, and set each phase to its correct status at that point.


Phase Summary Table

PhaseInputsOutputsReference
Discover.tf files, app source code, and/or billing exports (at least one required)gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json, .phase-status.json updated (outputs vary by input)references/phases/discover/discover.md
ClarifyDiscovery artifacts (gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json — whichever exist)preferences.json, .phase-status.json updatedreferences/phases/clarify/clarify.md
Designpreferences.json + discovery artifactsaws-design.json (infra), aws-design-ai.json (AI), aws-design-billing.json (billing-only)references/phases/design/design.md
Estimateaws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsonestimation-infra.json or estimation-ai.json or estimation-billing.json, .phase-status.json updatedreferences/phases/estimate/estimate.md
Generateestimation-infra.json or estimation-ai.json or estimation-billing.json, aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsongeneration-infra.json or generation-ai.json or generation-billing.json + terraform/, scripts/, ai-migration/, validation-report.json (when infra route active), MIGRATION_GUIDE.md, README.md, .phase-status.json updatedreferences/phases/generate/generate.md
Feedback.phase-status.json (discover completed minimum), all existing migration artifactsfeedback.json, trace.json, .phase-status.json updatedreferences/phases/feedback/feedback.md

MCP Servers

awspricing (for cost estimation):

  • Provides get_pricing, get_pricing_service_codes, get_pricing_service_attributes tools

  • Only needed during Estimate phase. Discover and Design do not require it.

  • Primary pricing source: references/shared/pricing-cache.md (cached 2026 rates, ±5-10% for infrastructure, ±15-25% for AI models). MCP is secondary — used only for services not found in the cache.


Files in This Skill

gcp-to-aws/
├── SKILL.md                                    ← You are here (orchestrator + state machine)
│
├── references/
│   ├── phases/
│   │   ├── discover/
│   │   │   ├── discover.md                     # Phase 1: Discover orchestrator
│   │   │   ├── discover-iac.md                 # Terraform/IaC discovery
│   │   │   ├── discover-app-code.md            # App code discovery
│   │   │   └── discover-billing.md             # Billing data discovery
│   │   ├── clarify/
│   │   │   ├── clarify.md                     # Phase 2: Clarify orchestrator
│   │   │   ├── clarify-global.md              # Category A: Global/Strategic (Q1-Q7)
│   │   │   ├── clarify-compute.md             # Categories B+C: Config Gaps + Compute (Q8-Q11)
│   │   │   ├── clarify-database.md            # Category D: Database (Q12–Q13b)
│   │   │   ├── clarify-ai.md                  # Category F: AI/Bedrock (Q14-Q22)
│   │   │   └── clarify-ai-only.md             # Standalone AI-only migration flow
│   │   ├── design/
│   │   │   ├── design.md                       # Phase 3: Design orchestrator
│   │   │   ├── design-infra.md                 # Infrastructure design (IaC-based)
│   │   │   ├── design-ai.md                    # AI workload design (Bedrock)
│   │   │   └── design-billing.md               # Billing-only design (fallback)
│   │   ├── estimate/
│   │   │   ├── estimate.md                     # Phase 4: Estimate orchestrator
│   │   │   ├── estimate-infra.md               # Infrastructure cost analysis
│   │   │   ├── estimate-ai.md                  # AI workload cost analysis
│   │   │   └── estimate-billing.md             # Billing-only cost analysis
│   │   ├── generate/
│   │   │   ├── generate.md                     # Phase 5: Generate orchestrator
│   │   │   ├── generate-infra.md               # Infrastructure migration plan
│   │   │   ├── generate-ai.md                  # AI migration plan
│   │   │   ├── generate-billing.md             # Billing-only migration plan
│   │   │   ├── generate-artifacts-infra.md     # Terraform configurations
│   │   │   ├── generate-artifacts-scripts.md  # Migration scripts
│   │   │   ├── generate-artifacts-ai.md        # Provider adapter + test harness
│   │   │   ├── generate-artifacts-billing.md   # Skeleton Terraform
│   │   │   └── generate-artifacts-docs.md      # MIGRATION_GUIDE.md + README.md
│   │   └── feedback/
│   │       ├── feedback.md                     # Phase 6: Feedback orchestrator
│   │       └── feedback-trace.md               # Anonymized trace builder
│   │
│   ├── design-refs/
│   │   ├── index.md                            # Lookup table: GCP type → design-ref file
│   │   ├── fast-path.md                        # Deterministic 1:1 mappings (Pass 1)
│   │   ├── compute.md                          # Compute mappings (Cloud Run, GCE, GKE, etc.)
│   │   ├── database.md                         # Database mappings (Cloud SQL, Spanner, etc.)
│   │   ├── storage.md                          # Storage mappings (GCS, Filestore, etc.)
│   │   ├── networking.md                       # Networking mappings (VPC, LB, DNS, etc.)
│   │   ├── messaging.md                        # Messaging mappings (Pub/Sub, etc.)
│   │   └── ai.md                               # AI mappings (Vertex AI → Bedrock)
│   │
│   ├── clustering/terraform/
│   │   ├── classification-rules.md             # Primary/secondary classification
│   │   ├── clustering-algorithm.md             # Cluster formation rules
│   │   ├── depth-calculation.md                # Topological depth calculation
│   │   └── typed-edges-strategy.md             # Edge type assignment
│   │
│   └── shared/
│       ├── schema-phase-status.md              # .phase-status.json schema (canonical reference)
│       ├── schema-discover-iac.md              # gcp-resource-inventory + clusters schemas (loaded by discover-iac.md)
│       ├── schema-discover-ai.md               # ai-workload-profile schema (loaded by discover-app-code.md and discover-iac.md Step 7d)
│       ├── schema-discover-billing.md          # billing-profile schema (loaded by discover-billing.md)
│       ├── schema-estimate-infra.md            # estimation-infra.json schema (loaded by estimate-infra.md at write time)
│       ├── handoff-gates.md                    # Fail-closed phase handoff protocol (GATE_FAIL / HANDOFF_OK)
│       ├── validate-artifacts.md               # Pre-report validation (Generate Step 0; read-only)
│       ├── migration-complexity.md             # Complexity tier definitions (small/medium/large) for timeline scaling
│       ├── pricing-cache.md                    # Cached AWS + source provider pricing (±5-25%, primary source)
│       └── bedrock-quotas.md                   # Bedrock TPM/RPM quota awareness, burndown rates, capacity planning
ConditionAction
No GCP sources found (no .tf, no app code, no billing data)Stop. Output: "No GCP sources detected. Provide at least one source type (Terraform files, application code, or billing exports) and try again."
.phase-status.json missing phase gateStop. Output: "Cannot enter Phase X: Phase Y-1 not completed. Start from Phase Y or resume Phase Y-1."
awspricing unavailable after 3 attemptsDisplay user warning about ±5-25% accuracy. Use pricing-cache.md. Add pricing_source: "cached_fallback" to the applicable estimation-*.json file.
User skips questions or says "use defaults for the rest"Apply documented defaults for remaining questions in the current batch and all subsequent batches. Phase 2 completes either way.
aws-design.json missing required clustersStop Phase 4. Output: "Re-run Phase 3 to generate missing cluster designs."

Defaults

  • IaC output: Terraform configurations, migration scripts, AI migration code, and documentation

  • Region: us-east-1 (unless user specifies, or GCP region → AWS region mapping suggests otherwise)

  • Sizing: Development tier (e.g., db.t4g.micro for databases, 0.5 CPU for Fargate)

  • Migration mode: Adapts based on available inputs (infrastructure, AI, or billing-only)

  • Cost currency: USD

  • Timeline assumption: 2-16 weeks depending on migration complexity — small (2-6 weeks), medium (6-12 weeks), large (12-18 weeks). See references/shared/migration-complexity.md for tier definitions.

Workflow Execution

When invoked, the agent MUST follow this exact sequence:

  1. Load phase status: Read .phase-status.json from .migration/*/.

    • If missing: Initialize for Phase 1 (Discover)

    • If exists: Determine current phase using deterministic rules in State Machine

  2. Determine phase to execute:

    • If current_phase exists: execute that phase.

    • Otherwise execute the first non-completed phase in ordered list: discover → clarify → design → estimate → generate.

    • If all ordered phases are completed: migration is complete (with feedback finalization rule).

  3. Read phase reference: Load the full reference file for the target phase.

  4. Execute ALL steps in order: Follow every numbered step in the reference file. Do not skip, optimize, or deviate.

  5. Validate outputs: Confirm all required output files exist with correct schema before proceeding. Phase orchestrators run Completion Handoff Gate checks per shared/handoff-gates.md.

  6. Handoff gate: Emit HANDOFF_OK or GATE_FAIL per shared/handoff-gates.md. On GATE_FAIL, stop — do not update phase status or load the next phase.

  7. Update phase status: Only after HANDOFF_OK. Use the Phase Status Update Protocol (read-merge-write) in the same turn as the phase's final output message.

  8. Feedback checkpoint: After a phase completes, check if feedback is due (see rules below). This runs before advancing to the next phase.

    • After Discover (if phases.feedback is "pending"): Output to user: "Would you like to share quick feedback (5 optional questions + anonymized usage data) to help improve this tool? Your data never includes resource names, file paths, or account IDs. [A] Send feedback now [B] Wait until after the Estimate phase"

      • If user picks A → Load references/phases/feedback/feedback.md, execute it, then continue to Clarify.

      • If user picks B → Continue to Clarify (feedback stays "pending").

    • After Estimate (if phases.feedback is "pending"): Output to user: "Would you like to share quick feedback now? (5 optional questions + anonymized usage data) [A] Yes, share feedback [B] No thanks, continue to Generate"

      • If user picks A → Load references/phases/feedback/feedback.md, execute it, then continue to Generate.

      • If user picks B → Use the Phase Status Update Protocol to set phases.feedback to "completed". Continue to Generate.

    • After Generate: No feedback offer. If phases.feedback is still "pending", use the Phase Status Update Protocol to set it to "completed" (user had two chances and chose to defer/skip).

  9. Display summary: Show user what was accomplished, highlight next phase, or confirm migration completion.

Critical constraint: Agent must strictly adhere to the reference file's workflow. If unable to complete a step, stop and report the exact step that failed.

User can invoke the skill again to resume from current_phase (or deterministic ordered evaluation when current_phase is absent).

Scope Notes

v1.0 includes:

  • Terraform infrastructure discovery

  • App code scanning (AI workload detection)

  • Billing data import from GCP

  • User requirement clarification (adaptive questions by category)

  • Multi-path Design (infrastructure, AI workloads, billing-only fallback)

  • AWS cost estimation (from pricing API or fallback)

  • Migration artifact generation (Terraform, scripts, AI adapters, documentation)

  • Optional feedback collection with anonymized telemetry

Files

references/design-refs/ai-openai-to-bedrock.md

OpenAI to Bedrock — Model Selection Guide

Applies to: OpenAI SDK usage detected in GCP-hosted applications → Amazon Bedrock

This file is loaded by design-ai.md when ai-workload-profile.json has summary.ai_source = "openai" or "both". It provides model mapping tables with pricing and honest competitive analysis for OpenAI → Bedrock migration decisions.

Many GCP-hosted applications use OpenAI's API rather than Vertex AI. This guide covers that migration path.

Verify all pricing via AWS Pricing MCP or references/shared/pricing-cache.md. Uses OpenAI Standard tier pricing.

Model lifecycle: Before recommending any Bedrock model, check references/shared/ai-model-lifecycle.md. Do not recommend Legacy models as primary selections for new migrations. Legacy models are annotated below where they appear.


Key Insight: The Landscape Has Changed (April 2026)

It is no longer "Bedrock is always cheaper." It depends on the model.

  • Bedrock cheaper: GPT-5.5 flagship (17% cheaper output via Opus 4.6), Nova Lite vs Mini models (85-94%), Nova Micro vs Nano (65-87%), Nova 2 Pro vs Pro models (90-95%), DeepSeek-R1 vs o3 (32%)

  • OpenAI cheaper: GPT-5.4 (5%), GPT-5.2 (50%), GPT-5.1/5 (40%), GPT-4.1 (43%), GPT-4o (29%), o4-mini/o3-mini/o1-mini (69%)

GPT-5.5 note (April 23, 2026): GPT-5.5 doubled pricing to $5/$30 per MTok vs GPT-5.4's $2.50/$15. Claude Opus 4.6 at $5/$25 now matches on input and is 17% cheaper on output. This reverses the GPT-5.4 dynamic where OpenAI was cheaper — at the GPT-5.5 tier, Bedrock wins on cost. GPT-5.5 uses 40% fewer output tokens on coding tasks (per OpenAI), partially offsetting the price hike for Codex-style workloads.


Model Mapping Tables

GPT-5.5 Series (Latest — April 23, 2026)

GPT-5.5 is the first fully retrained base model since GPT-4.5. Natively omnimodal (text + image + audio + video), 88.7% SWE-Bench Verified, 256K context in ChatGPT / 1M in API. Two variants: standard and Pro. No Mini/Nano variants at launch (expected Q3 2026). Percentages below are blended savings using a 2:1 input-to-output token ratio.

OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
GPT-5.5$5.00 / $30.00Claude Opus 4.6$5.00 / $25.00Bedrock 17% cheaper
GPT-5.5$5.00 / $30.00Claude Sonnet 4.6$3.00 / $15.00Bedrock 53% cheaper
GPT-5.5 Pro$30.00 / $180.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 95% cheaper

Token efficiency caveat: OpenAI reports GPT-5.5 uses ~40% fewer output tokens on Codex-style tasks vs GPT-5.4. Effective cost increase over GPT-5.4 is ~50% (not 100%) for coding workloads. For non-coding workloads, the full 2× price applies.

GPT-5.4 Series

Percentages below are blended savings using a 2:1 input-to-output token ratio. GPT-5.4 uses breakpoint pricing at 272K input tokens; rates below assume <272K context.

OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
GPT-5.4$2.50 / $15.00Claude Sonnet 4.6$3.00 / $15.00OpenAI 5% cheaper
GPT-5.4 Mini$0.75 / $4.50Nova Lite$0.06 / $0.24Bedrock 94% cheaper
GPT-5.4 Nano$0.20 / $1.25Nova Micro$0.035 / $0.14Bedrock 87% cheaper
GPT-5.4 Pro$30.00 / $180.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 94% cheaper
Flagship (GPT-5/5.2 Series)

Percentages below are blended savings using a 2:1 input-to-output token ratio.

OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
GPT-5.2$1.75 / $14.00Claude Opus 4.7 / 4.6$5.00 / $25.00OpenAI 50% cheaper
GPT-5.1 / GPT-5$1.25 / $10.00Claude Sonnet 4.6$3.00 / $15.00OpenAI 40% cheaper
GPT-5 Mini$0.25 / $2.00Nova Lite$0.06 / $0.24Bedrock 86% cheaper
GPT-5 Nano$0.05 / $0.40Nova Micro$0.035 / $0.14Bedrock 58% cheaper
Pro Models (Extended Reasoning)

Lifecycle note: Nova Premier v1 is Legacy (EOL Sep 14, 2026). Nova 2 Pro (Preview) is the Active successor for reasoning-heavy workloads. Pricing differs — see pricing-cache.md.

OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
GPT-5.5 Pro$30.00 / $180.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 95% cheaper
GPT-5.4 Pro$30.00 / $180.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 94% cheaper
GPT-5.2 Pro$21.00 / $168.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 93% cheaper
GPT-5 Pro$15.00 / $120.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 90% cheaper
GPT-4.1 Series
OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
GPT-4.1$2.00 / $8.00Claude Sonnet 4.6$3.00 / $15.00OpenAI 43% cheaper
GPT-4.1 Mini$0.40 / $1.60Nova Lite$0.06 / $0.24Bedrock 85% cheaper
GPT-4.1 Nano$0.10 / $0.40Nova Micro$0.035 / $0.14Bedrock 65% cheaper
GPT-4o Series
OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
GPT-4o$2.50 / $10.00Claude Sonnet 4.6$3.00 / $15.00OpenAI 29% cheaper
GPT-4o Mini$0.15 / $0.60Nova Lite$0.06 / $0.24Bedrock 60% cheaper
Reasoning Models (o-series)

Lifecycle note: Nova Premier v1 is Legacy (EOL Sep 14, 2026). Table below uses Nova 2 Pro (Preview) as the Active replacement.

OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
o1-pro$150.00 / $600.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 98% cheaper
o3-pro$20.00 / $80.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 87% cheaper
o1$15.00 / $60.00Nova 2 Pro (Preview)$1.38 / $11.00Bedrock 83% cheaper
o3$2.00 / $8.00DeepSeek-R1$1.35 / $5.40Bedrock 32% cheaper
o4-mini / o3-mini / o1-mini$1.10 / $4.40Claude Sonnet 4.6$3.00 / $15.00OpenAI 69% cheaper
Legacy Models
OpenAI ModelPrice (in/out per 1M)Best Bedrock MatchBedrock PriceWinner
GPT-4 Turbo$10.00 / $30.00Claude Sonnet 4.6$3.00 / $15.00Bedrock 58% cheaper
GPT-4$30.00 / $60.00Claude Sonnet 4.6$3.00 / $15.00Bedrock 82% cheaper
GPT-3.5 Turbo$0.50 / $1.50Llama 4 Maverick$0.24 / $0.97Bedrock 42% cheaper + much better quality
OpenAI Models on Bedrock (gpt-oss)

OpenAI's open-source models are available directly on Bedrock, enabling migration without switching model families:

OpenAI ModelPrice (in/out per 1M)Bedrock gpt-ossBedrock PriceNotes
GPT-4o Mini$0.15 / $0.60gpt-oss-120b$0.15 / $0.60Same cost, runs on AWS infrastructure
GPT-5 Nano$0.05 / $0.40gpt-oss-20b$0.07 / $0.30Similar budget tier on AWS

This path avoids model-family risk: the application stays on OpenAI-architecture models while consolidating on AWS infrastructure.

Percentages are blended savings using a 2:1 input-to-output token ratio. Actual savings depend on your input/output ratio.


Migration Decision Framework

Migrate to Bedrock if:

  • Using GPT-5.5 flagship → Bedrock 17% cheaper on output via Opus 4.6 ($5/$25 vs $5/$30); Sonnet 4.6 is 53% cheaper

  • Using Pro/expensive models (GPT-5.5 Pro, GPT-5.4 Pro, o1-pro) → 87-98% savings via Nova 2 Pro

  • Using Mini/Nano models at high volume → 87-94% savings via Nova Lite/Micro

  • Using legacy GPT-4/3.5 → 42-82% savings

  • Need AWS infrastructure integration

  • Need prompt caching (Claude only, 90% savings on cached content)

  • Using o3 for reasoning → DeepSeek-R1 on Bedrock is 32% cheaper

  • Want to stay on OpenAI models → gpt-oss on Bedrock (same models, AWS infrastructure)

Consider staying on OpenAI if:

  • Using GPT-5.5 for omnimodal (audio/video) → Claude is text+image only; GPT-5.5 has native audio/video

  • Using GPT-5.4 flagship → only 5% cheaper than Sonnet 4.6; marginal either way

  • Using mid-tier flagships (GPT-5, GPT-4.1, o3, o4-mini) → OpenAI 29-69% cheaper

  • Low volume (<$500/mo) where absolute savings are small

  • Heavily integrated with OpenAI ecosystem (Assistants API, gpt-image, Whisper, Realtime)

  • Need Realtime API (no Bedrock equivalent)

Analyze carefully: Calculate actual token usage x model-specific pricing. Small % differences matter at scale.


Feature Migration

OpenAI FeatureBedrock EquivalentNotes
OpenAI SDK (direct)Mantle OpenAI-compat endpointsZero code changes — set OPENAI_BASE_URL + API key + model ID
Function callingClaude tools (excellent, similar format)Minimal changes (works via Mantle or Converse API)
StreamingAll major modelsVerify gateway format
Vision (GPT-4V)Claude Sonnet/Haiku, Llama 4 Maverick70-95% cheaper
Embeddings (ada-002)Titan Embeddings ($0.02/1M, 1536 dims)Must re-embed all docs
DALL-E / gpt-imageNova Canvas ($0.04-$0.08/img)DALL-E EOL May 12, 2026; OpenAI replacement is gpt-image-1.5; Titan Image Gen v2 is Legacy (EOL Jun 30, 2026); use Nova Canvas
Whisper (STT)Amazon Transcribe ($0.024/min)4x more expensive but more features
TTSAmazon PollyDifferent pricing model
Assistants APISee Assistants API decision tree belowPath depends on which Assistants features are used — see decision tree
JSON modeClaude (excellent), Nova Pro (good)Most models via prompt
Realtime APINo equivalentStay on OpenAI for this

Common Migration Paths

OpenAI SDK → Mantle (minimal code changes)

If the application uses the OpenAI Python/JS SDK directly (from openai import OpenAI / new OpenAI()), Bedrock's Mantle OpenAI-compatible endpoints allow migration with minimal code changes — primarily environment variables plus a model string swap:

  1. Set OPENAI_BASE_URL=https://bedrock-mantle.{region}.api.aws/v1

  2. Set OPENAI_API_KEY=<bedrock-api-key> — use a Bedrock API key, not your existing OpenAI API key

  3. Change model string (e.g., gpt-5.4 → anthropic.claude-sonnet-4-6 or openai.gpt-oss-120b)

Hard gates before recommending Mantle:

  • Model compatibility: Verify the selected Bedrock model supports the Responses API — check API compatibility. Not all models do. Do not recommend Mantle Responses API unless the target model is confirmed compatible.

  • Region availability: Mantle is available in 13 regions (us-east-1, us-east-2, us-west-2, ap-northeast-1, ap-south-1, ap-southeast-2, ap-southeast-3, eu-central-1, eu-west-1, eu-west-2, eu-south-1, eu-north-1, sa-east-1). If the target region is outside this list, do not recommend Mantle — use the boto3 Converse API path instead.

Supports Chat Completions API, Responses API, streaming, and stateful conversations.

Responses API capabilities (when stateful conversations matter):

  • Stateful conversation management — Bedrock rebuilds context automatically; no need to pass full conversation history on each request

  • Async / long-running inference — background processing for workloads that exceed typical request timeouts (useful for complex agentic tasks)

  • Streaming + non-streaming — both modes supported via the same endpoint

Assistants API → Migration Decision Tree

Assistants API and Responses API are different surfaces. Do not treat all Assistants API usage as an env-var-only migration. Apply this decision tree:

1. App already uses OpenAI Responses API (responses.create) → Mantle is the cleanest path. Env var swap + model string change. Minimal code changes.

2. App uses Assistants API only for stateful multi-turn conversation (no hosted tools, no file search, no code interpreter, no persistent Assistant objects, no complex run lifecycle) → Mantle Responses API is viable. Requires migrating from threads/runs calls to responses.create — this is a small API migration (days), not a full redesign. Not a zero-code-change swap.

3. App uses Assistants API with simple hosted tools (function calling only, no file search or code interpreter) → Mantle Responses API with tool use is viable. Moderate code migration (1-2 weeks) to adapt tool definitions and run lifecycle.

4. App uses Assistants API with file search, vector stores, code interpreter, persistent Assistant objects, or complex run lifecycle management → Do not recommend Mantle. Evaluate: Bedrock Agents (sessions, action groups, knowledge bases) for full agentic replacement (2-4 week migration), or app-managed orchestration if the team prefers to own state.

When to prefer Converse API over Mantle: If you need Bedrock-specific features (Guardrails, Knowledge Bases, prompt caching, Bedrock Agents integration) or your target region doesn't have Mantle. Mantle is the fastest path; Converse API is the most feature-complete path.

GPT-5.4 → Claude Sonnet 4.6

Near price parity (~5% difference). Migration case is driven by AWS consolidation, agentic reliability, or prompt caching — not cost. Both have ~200K+ context. Low risk.

GPT-5.4 Mini/Nano → Nova Lite/Micro

87-94% savings. Strong cost case at any volume. Nova Lite (300K context) covers most GPT-5.4 Mini use cases.

GPT-4/4 Turbo → Claude Sonnet 4.6

70-90% savings, similar or better quality, longer context (200K vs 128K). Low risk.

GPT-3.5 Turbo → Llama 4 Maverick

Similar cost, dramatically better quality, 1M context (vs 16K).

GPT-4 → Multi-Model (high spend)

Tier by complexity: simple → Nova Micro/Llama 4 Scout (60%), moderate → Llama 4 Maverick/Nova Pro (30%), complex → Claude Sonnet (10%). 85-95% savings.

Pro models → Nova 2 Pro

83-98% savings. Strong migration case at any volume. (Nova Premier v1 is Legacy — use Nova 2 Pro instead.)


Volume-Based Recommendations

Low (<1M tokens/day): Use best model for quality. Cost difference minimal.

Medium (1-10M tokens/day): Present cost comparison at volume. At 5M input + 2.5M output/day, evaluate per-model economics carefully.

High (10-100M tokens/day): Multi-model tiered approach recommended. Route by task complexity.

Very high (>100M tokens/day): Mandatory tiering:

  • Simple tasks (60%) → Nova Micro or Llama 4 Scout

  • Moderate tasks (30%) → Llama 4 Maverick or Nova Pro

  • Complex tasks (10%) → Claude Sonnet 4.6


OpenAI Pricing Tiers

OpenAI offers 4 tiers: Batch (50% off, 24hr), Flex (30-50% off, higher latency), Standard (baseline), Priority (2x, lowest latency). This guide uses Standard tier for comparison.

Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Names Claude Code tools, Needs an MCP server

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